Score thresholding for accurate instance classification in multiple instance learning

Abstract

Multiple instance learning (MIL) is a form of weakly supervised learning for problems in which training instances are arranged into bags, and a label is provided for whole bags but not for individual instances. Most proposed MIL algorithms focus on bag classification, but more recently, the classification of individual instances has attracted the attention of the pattern recognition community. While these two tasks are similar, there are important differences in the consequences of instance misclassification. In this paper, the scoring function learned by MIL classifiers for the bag classification task is exploited for instance classification by adjusting the decision threshold. A new criterion for the threshold adjustment is proposed and validated using 7 reference MIL algorithms on 3 real-world data sets from different application domains. Experiments show considerable improvements in accuracy over these algorithms for instance classification. In some applications, the unweighted average recall increases by as much as 18%, while the Fi-score increases by 12%.